475 lines
26 KiB
Objective-C
475 lines
26 KiB
Objective-C
//
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// This file is auto-generated. Please don't modify it!
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//
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#pragma once
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#ifdef __cplusplus
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//#import "opencv.hpp"
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#import "opencv2/features2d.hpp"
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#else
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#define CV_EXPORTS
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#endif
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#import <Foundation/Foundation.h>
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#import "Feature2D.h"
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// C++: enum ScoreType (cv.ORB.ScoreType)
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typedef NS_ENUM(int, ScoreType) {
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ORB_HARRIS_SCORE NS_SWIFT_NAME(HARRIS_SCORE) = 0,
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ORB_FAST_SCORE NS_SWIFT_NAME(FAST_SCORE) = 1
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};
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NS_ASSUME_NONNULL_BEGIN
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// C++: class ORB
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/**
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* Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
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*
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* described in CITE: RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects
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* the strongest features using FAST or Harris response, finds their orientation using first-order
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* moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or
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* k-tuples) are rotated according to the measured orientation).
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*
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* Member of `Features2d`
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*/
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CV_EXPORTS @interface ORB : Feature2D
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#ifdef __cplusplus
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@property(readonly)cv::Ptr<cv::ORB> nativePtrORB;
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#endif
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#ifdef __cplusplus
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- (instancetype)initWithNativePtr:(cv::Ptr<cv::ORB>)nativePtr;
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+ (instancetype)fromNative:(cv::Ptr<cv::ORB>)nativePtr;
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#endif
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#pragma mark - Methods
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//
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// static Ptr_ORB cv::ORB::create(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K = 2, ORB_ScoreType scoreType = ORB::HARRIS_SCORE, int patchSize = 31, int fastThreshold = 20)
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//
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/**
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* The ORB constructor
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*
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* @param nfeatures The maximum number of features to retain.
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* @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
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* pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
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* will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
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* will mean that to cover certain scale range you will need more pyramid levels and so the speed
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* will suffer.
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* @param nlevels The number of pyramid levels. The smallest level will have linear size equal to
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* input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
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* @param edgeThreshold This is size of the border where the features are not detected. It should
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* roughly match the patchSize parameter.
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* @param firstLevel The level of pyramid to put source image to. Previous layers are filled
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* with upscaled source image.
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* @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
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* default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
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* so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
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* random points (of course, those point coordinates are random, but they are generated from the
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* pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
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* rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
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* output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
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* denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
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* bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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* @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
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* (the score is written to KeyPoint::score and is used to retain best nfeatures features);
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* FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
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* but it is a little faster to compute.
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* @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
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* pyramid layers the perceived image area covered by a feature will be larger.
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* @param fastThreshold the fast threshold
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*/
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+ (ORB*)create:(int)nfeatures scaleFactor:(float)scaleFactor nlevels:(int)nlevels edgeThreshold:(int)edgeThreshold firstLevel:(int)firstLevel WTA_K:(int)WTA_K scoreType:(ScoreType)scoreType patchSize:(int)patchSize fastThreshold:(int)fastThreshold NS_SWIFT_NAME(create(nfeatures:scaleFactor:nlevels:edgeThreshold:firstLevel:WTA_K:scoreType:patchSize:fastThreshold:));
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/**
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* The ORB constructor
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*
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* @param nfeatures The maximum number of features to retain.
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* @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
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* pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
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* will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
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* will mean that to cover certain scale range you will need more pyramid levels and so the speed
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* will suffer.
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* @param nlevels The number of pyramid levels. The smallest level will have linear size equal to
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* input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
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* @param edgeThreshold This is size of the border where the features are not detected. It should
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* roughly match the patchSize parameter.
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* @param firstLevel The level of pyramid to put source image to. Previous layers are filled
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* with upscaled source image.
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* @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
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* default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
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* so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
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* random points (of course, those point coordinates are random, but they are generated from the
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* pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
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* rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
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* output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
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* denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
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* bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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* @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
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* (the score is written to KeyPoint::score and is used to retain best nfeatures features);
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* FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
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* but it is a little faster to compute.
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* @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
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* pyramid layers the perceived image area covered by a feature will be larger.
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*/
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+ (ORB*)create:(int)nfeatures scaleFactor:(float)scaleFactor nlevels:(int)nlevels edgeThreshold:(int)edgeThreshold firstLevel:(int)firstLevel WTA_K:(int)WTA_K scoreType:(ScoreType)scoreType patchSize:(int)patchSize NS_SWIFT_NAME(create(nfeatures:scaleFactor:nlevels:edgeThreshold:firstLevel:WTA_K:scoreType:patchSize:));
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/**
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* The ORB constructor
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*
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* @param nfeatures The maximum number of features to retain.
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* @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
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* pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
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* will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
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* will mean that to cover certain scale range you will need more pyramid levels and so the speed
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* will suffer.
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* @param nlevels The number of pyramid levels. The smallest level will have linear size equal to
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* input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
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* @param edgeThreshold This is size of the border where the features are not detected. It should
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* roughly match the patchSize parameter.
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* @param firstLevel The level of pyramid to put source image to. Previous layers are filled
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* with upscaled source image.
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* @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
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* default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
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* so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
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* random points (of course, those point coordinates are random, but they are generated from the
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* pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
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* rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
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* output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
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* denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
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* bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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* @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
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* (the score is written to KeyPoint::score and is used to retain best nfeatures features);
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* FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
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* but it is a little faster to compute.
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* pyramid layers the perceived image area covered by a feature will be larger.
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*/
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+ (ORB*)create:(int)nfeatures scaleFactor:(float)scaleFactor nlevels:(int)nlevels edgeThreshold:(int)edgeThreshold firstLevel:(int)firstLevel WTA_K:(int)WTA_K scoreType:(ScoreType)scoreType NS_SWIFT_NAME(create(nfeatures:scaleFactor:nlevels:edgeThreshold:firstLevel:WTA_K:scoreType:));
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/**
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* The ORB constructor
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*
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* @param nfeatures The maximum number of features to retain.
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* @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
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* pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
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* will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
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* will mean that to cover certain scale range you will need more pyramid levels and so the speed
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* will suffer.
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* @param nlevels The number of pyramid levels. The smallest level will have linear size equal to
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* input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
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* @param edgeThreshold This is size of the border where the features are not detected. It should
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* roughly match the patchSize parameter.
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* @param firstLevel The level of pyramid to put source image to. Previous layers are filled
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* with upscaled source image.
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* @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
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* default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
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* so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
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* random points (of course, those point coordinates are random, but they are generated from the
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* pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
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* rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
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* output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
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* denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
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* bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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* (the score is written to KeyPoint::score and is used to retain best nfeatures features);
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* FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
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* but it is a little faster to compute.
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* pyramid layers the perceived image area covered by a feature will be larger.
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*/
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+ (ORB*)create:(int)nfeatures scaleFactor:(float)scaleFactor nlevels:(int)nlevels edgeThreshold:(int)edgeThreshold firstLevel:(int)firstLevel WTA_K:(int)WTA_K NS_SWIFT_NAME(create(nfeatures:scaleFactor:nlevels:edgeThreshold:firstLevel:WTA_K:));
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/**
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* The ORB constructor
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*
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* @param nfeatures The maximum number of features to retain.
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* @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
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* pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
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* will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
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* will mean that to cover certain scale range you will need more pyramid levels and so the speed
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* will suffer.
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* @param nlevels The number of pyramid levels. The smallest level will have linear size equal to
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* input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
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* @param edgeThreshold This is size of the border where the features are not detected. It should
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* roughly match the patchSize parameter.
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* @param firstLevel The level of pyramid to put source image to. Previous layers are filled
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* with upscaled source image.
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* default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
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* so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
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* random points (of course, those point coordinates are random, but they are generated from the
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* pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
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* rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
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* output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
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* denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
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* bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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* (the score is written to KeyPoint::score and is used to retain best nfeatures features);
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* FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
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* but it is a little faster to compute.
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* pyramid layers the perceived image area covered by a feature will be larger.
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*/
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+ (ORB*)create:(int)nfeatures scaleFactor:(float)scaleFactor nlevels:(int)nlevels edgeThreshold:(int)edgeThreshold firstLevel:(int)firstLevel NS_SWIFT_NAME(create(nfeatures:scaleFactor:nlevels:edgeThreshold:firstLevel:));
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/**
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* The ORB constructor
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*
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* @param nfeatures The maximum number of features to retain.
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* @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
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* pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
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* will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
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* will mean that to cover certain scale range you will need more pyramid levels and so the speed
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* will suffer.
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* @param nlevels The number of pyramid levels. The smallest level will have linear size equal to
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* input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
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* @param edgeThreshold This is size of the border where the features are not detected. It should
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* roughly match the patchSize parameter.
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* with upscaled source image.
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* default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
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* so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
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* random points (of course, those point coordinates are random, but they are generated from the
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* pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
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* rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
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* output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
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* denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
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* bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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* (the score is written to KeyPoint::score and is used to retain best nfeatures features);
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* FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
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* but it is a little faster to compute.
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* pyramid layers the perceived image area covered by a feature will be larger.
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*/
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+ (ORB*)create:(int)nfeatures scaleFactor:(float)scaleFactor nlevels:(int)nlevels edgeThreshold:(int)edgeThreshold NS_SWIFT_NAME(create(nfeatures:scaleFactor:nlevels:edgeThreshold:));
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/**
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* The ORB constructor
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*
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* @param nfeatures The maximum number of features to retain.
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* @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
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* pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
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* will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
|
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* will mean that to cover certain scale range you will need more pyramid levels and so the speed
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* will suffer.
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* @param nlevels The number of pyramid levels. The smallest level will have linear size equal to
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* input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
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* roughly match the patchSize parameter.
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* with upscaled source image.
|
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* default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
|
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* so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
|
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* random points (of course, those point coordinates are random, but they are generated from the
|
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* pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
|
|
* rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
|
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* output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
|
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* denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
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* bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
|
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* (the score is written to KeyPoint::score and is used to retain best nfeatures features);
|
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* FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
|
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* but it is a little faster to compute.
|
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* pyramid layers the perceived image area covered by a feature will be larger.
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*/
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+ (ORB*)create:(int)nfeatures scaleFactor:(float)scaleFactor nlevels:(int)nlevels NS_SWIFT_NAME(create(nfeatures:scaleFactor:nlevels:));
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/**
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* The ORB constructor
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*
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* @param nfeatures The maximum number of features to retain.
|
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* @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
|
|
* pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
|
|
* will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
|
|
* will mean that to cover certain scale range you will need more pyramid levels and so the speed
|
|
* will suffer.
|
|
* input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
|
|
* roughly match the patchSize parameter.
|
|
* with upscaled source image.
|
|
* default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
|
|
* so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
|
|
* random points (of course, those point coordinates are random, but they are generated from the
|
|
* pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
|
|
* rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
|
|
* output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
|
|
* denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
|
|
* bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
|
|
* (the score is written to KeyPoint::score and is used to retain best nfeatures features);
|
|
* FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
|
|
* but it is a little faster to compute.
|
|
* pyramid layers the perceived image area covered by a feature will be larger.
|
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*/
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+ (ORB*)create:(int)nfeatures scaleFactor:(float)scaleFactor NS_SWIFT_NAME(create(nfeatures:scaleFactor:));
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/**
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* The ORB constructor
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*
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* @param nfeatures The maximum number of features to retain.
|
|
* pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
|
|
* will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
|
|
* will mean that to cover certain scale range you will need more pyramid levels and so the speed
|
|
* will suffer.
|
|
* input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
|
|
* roughly match the patchSize parameter.
|
|
* with upscaled source image.
|
|
* default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
|
|
* so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
|
|
* random points (of course, those point coordinates are random, but they are generated from the
|
|
* pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
|
|
* rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
|
|
* output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
|
|
* denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
|
|
* bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
|
|
* (the score is written to KeyPoint::score and is used to retain best nfeatures features);
|
|
* FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
|
|
* but it is a little faster to compute.
|
|
* pyramid layers the perceived image area covered by a feature will be larger.
|
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*/
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+ (ORB*)create:(int)nfeatures NS_SWIFT_NAME(create(nfeatures:));
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/**
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* The ORB constructor
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*
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* pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
|
|
* will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
|
|
* will mean that to cover certain scale range you will need more pyramid levels and so the speed
|
|
* will suffer.
|
|
* input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
|
|
* roughly match the patchSize parameter.
|
|
* with upscaled source image.
|
|
* default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
|
|
* so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
|
|
* random points (of course, those point coordinates are random, but they are generated from the
|
|
* pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
|
|
* rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
|
|
* output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
|
|
* denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
|
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* bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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* (the score is written to KeyPoint::score and is used to retain best nfeatures features);
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* FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
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* but it is a little faster to compute.
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* pyramid layers the perceived image area covered by a feature will be larger.
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*/
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+ (ORB*)create NS_SWIFT_NAME(create());
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//
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// void cv::ORB::setMaxFeatures(int maxFeatures)
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//
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- (void)setMaxFeatures:(int)maxFeatures NS_SWIFT_NAME(setMaxFeatures(maxFeatures:));
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//
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// int cv::ORB::getMaxFeatures()
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//
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- (int)getMaxFeatures NS_SWIFT_NAME(getMaxFeatures());
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//
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// void cv::ORB::setScaleFactor(double scaleFactor)
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//
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- (void)setScaleFactor:(double)scaleFactor NS_SWIFT_NAME(setScaleFactor(scaleFactor:));
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//
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// double cv::ORB::getScaleFactor()
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//
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- (double)getScaleFactor NS_SWIFT_NAME(getScaleFactor());
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//
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// void cv::ORB::setNLevels(int nlevels)
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//
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- (void)setNLevels:(int)nlevels NS_SWIFT_NAME(setNLevels(nlevels:));
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//
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// int cv::ORB::getNLevels()
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//
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- (int)getNLevels NS_SWIFT_NAME(getNLevels());
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//
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// void cv::ORB::setEdgeThreshold(int edgeThreshold)
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//
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- (void)setEdgeThreshold:(int)edgeThreshold NS_SWIFT_NAME(setEdgeThreshold(edgeThreshold:));
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//
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// int cv::ORB::getEdgeThreshold()
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//
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- (int)getEdgeThreshold NS_SWIFT_NAME(getEdgeThreshold());
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//
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// void cv::ORB::setFirstLevel(int firstLevel)
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//
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- (void)setFirstLevel:(int)firstLevel NS_SWIFT_NAME(setFirstLevel(firstLevel:));
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//
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// int cv::ORB::getFirstLevel()
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//
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- (int)getFirstLevel NS_SWIFT_NAME(getFirstLevel());
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//
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// void cv::ORB::setWTA_K(int wta_k)
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//
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- (void)setWTA_K:(int)wta_k NS_SWIFT_NAME(setWTA_K(wta_k:));
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//
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// int cv::ORB::getWTA_K()
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//
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- (int)getWTA_K NS_SWIFT_NAME(getWTA_K());
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//
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// void cv::ORB::setScoreType(ORB_ScoreType scoreType)
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//
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|
- (void)setScoreType:(ScoreType)scoreType NS_SWIFT_NAME(setScoreType(scoreType:));
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|
|
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//
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|
// ORB_ScoreType cv::ORB::getScoreType()
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|
//
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|
- (ScoreType)getScoreType NS_SWIFT_NAME(getScoreType());
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|
|
|
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//
|
|
// void cv::ORB::setPatchSize(int patchSize)
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|
//
|
|
- (void)setPatchSize:(int)patchSize NS_SWIFT_NAME(setPatchSize(patchSize:));
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|
|
|
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|
//
|
|
// int cv::ORB::getPatchSize()
|
|
//
|
|
- (int)getPatchSize NS_SWIFT_NAME(getPatchSize());
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|
|
|
|
|
//
|
|
// void cv::ORB::setFastThreshold(int fastThreshold)
|
|
//
|
|
- (void)setFastThreshold:(int)fastThreshold NS_SWIFT_NAME(setFastThreshold(fastThreshold:));
|
|
|
|
|
|
//
|
|
// int cv::ORB::getFastThreshold()
|
|
//
|
|
- (int)getFastThreshold NS_SWIFT_NAME(getFastThreshold());
|
|
|
|
|
|
//
|
|
// String cv::ORB::getDefaultName()
|
|
//
|
|
- (NSString*)getDefaultName NS_SWIFT_NAME(getDefaultName());
|
|
|
|
|
|
|
|
@end
|
|
|
|
NS_ASSUME_NONNULL_END
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|